Director, AI & Automation - Shared Services in London

We are hiring a Director, AI & Automation - Shared Services to build and scale AI‑enabled efficiency across Finance, HR, Legal, and Marketing. This is a player‑coach role: you will be expected to personally design and deliver automation and AI solutions, while also owning the operating model (intake, prioritization, governance, and benefits tracking).

In the first 6-12 months, you should expect to spend a significant portion of your time building (coding, configuring automations, integrating systems, and hardening solutions for production). You will have the opportunity to recruit an AI Automation Engineer after you start; however, this role requires hands‑on technical depth from day one.

The ideal candidate will exhibit the ability to maintain a high level of confidentiality, organisation, attention to detail, initiative, autonomy, and judgment, as well as an ability to effectively prioritise workflow and execute assignments. This is a dynamic role that requires responsiveness and comfort working in a fast‑paced environment.

In this role, success is measured by outcomes and adoption, not experimentation for its own sake:

• 15‑30% cycle‑time reduction on targeted processes, with manual touches and exception rates reduced
• High adoption and sustainable change (target: >80% adoption in pilot groups where appropriate)
• Audit‑ready controls embedded (e.g., access control, audit logging, evidence trails, and documented SOPs)
• Benefits realised and reported quarterly (hours returned, annualised net benefits, quality, and risk reductions)
• Dual OKRs: business outcomes (cycle time, hours returned, adoption, audit findings) and platform outcomes (reliability/SLA, cost per run, zero privacy incidents) in partnership with IT Solutions

This role can be based at our offices near London Bridge or Bournemouth, with 2‑3 days in the office.

What You Will Work On

• Hands‑on build and delivery (primary)
• Identify, prototype, and ship AI and automation solutions from discovery through MVP and scale (LLM workflows, RPA, workflow automation, integrations, and lightweight internal apps)
• Write production‑quality code where needed (primarily Python) and build reliable integrations (APIs, webhooks, ETL‑style data movement, document pipelines)
• Design and implement LLM‑powered features such as document intake/summarisation, classification and routing, knowledge retrieval, exception handling, and agent‑assisted operations— with clear quality gates and evaluation
• Build automation that fits real operating constraints: retries, idempotency, monitoring/alerts, and clear ownership for ongoing support
• Create and maintain reusable building blocks (templates, libraries, prompt/eval harnesses, connectors) to accelerate delivery across use cases

Portfolio, ROI, and governance (shared services leadership)

• Own the use‑case intake process, prioritisation rubric, and shared backlog across Finance, HR, Legal, and Marketing
• Run a joint weekly backlog review with IT Solutions to align on capacity, dependencies, and delivery sequencing
• Baseline current‑state metrics (cycle time, effort, error rates) and build business cases with clear assumptions and benefits tracking
• Provide monthly steering updates (progress, risks, decisions needed) and quarterly benefits reporting
• Coordinate UAT and rollout with process owners; ensure SOPs, training materials, and controls documentation are updated for sustainable adoption

Controls‑first delivery with IT Solutions

• Partner with IT Solutions to design solutions that meet security and platform guardrails (SDLC, identity/RBAC, secrets management, data protection, and change control)
• Implement definition‑of‑done gates for AI automation releases: threat model, RBAC, audit logging, evaluation/red‑team results as applicable, rollback plan, runbooks, and observability
• Coordinate with IT change control processes (e.g., CAB) where required, and ensure releases are supportable with clear ownership and monitoring
• Ensure vendor/tooling choices and data flows are compliant with relevant policies and regulations (e.g., SOX controls where applicable, GDPR/data privacy requirements)

Team building and capability development

• Recruit, mentor, and lead additional capacity (e.g., an AI Automation Engineer) as the portfolio scales; set standards for code quality and delivery hygiene
• Build strong working relationships across Shared Services and IT, creating clear handoffs and support models (who owns what post‑launch)
• Drive practical enablement for teams: patterns, documentation, and best practices to increase automation literacy and adoption

Example focus areas: Finance: AP/AR workflows, month‑end close support, reconciliations, invoice and